Reddy, Vikas and Farr, Anna Charisse and Wu, Paul and Mengersen, Kerrie and Yarlagadda, Prasad K. D. V. (2014) An intuitive dashboard for Bayesian Network inference. In: 2nd International Conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE 2013), 1-5 Sept 2013, Prague; Czech Republic.
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Abstract
Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.
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| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Paper) |
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| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Date Deposited: | 28 Nov 2019 00:34 |
| Last Modified: | 09 Oct 2020 05:01 |
| Uncontrolled Keywords: | Baynesian networks; Baynesian Network inference; end users; C++ (programming language); application programs; integrated circuits; software packages |
| Fields of Research (2008): | 12 Built Environment and Design > 1299 Other Built Environment and Design > 129999 Built Environment and Design not elsewhere classified 01 Mathematical Sciences > 0104 Statistics > 010499 Statistics not elsewhere classified |
| Fields of Research (2020): | 33 BUILT ENVIRONMENT AND DESIGN > 3399 Other built environment and design > 339999 Other built environment and design not elsewhere classified 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490599 Statistics not elsewhere classified |
| Identification Number or DOI: | https://doi.org/10.1088/1742-6596/490/1/012023 |
| URI: | http://eprints.usq.edu.au/id/eprint/36047 |
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